Controllable Neural Dialogue Summarization with Personal Named Entity Planning

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Controllable Neural Dialogue Summarization with Personal Named Entity Planning
Title:
Controllable Neural Dialogue Summarization with Personal Named Entity Planning
Journal Title:
Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing
DOI:
Keywords:
Publication Date:
11 November 2021
Citation:
Liu, ZY., Chen, N. Controllable Neural Dialogue Summarization with Personal Named Entity Planning. Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing. 2021
Abstract:
In this paper, we propose a controllable neural generation framework that can flexibly guide dialogue summarization with personal named entity planning. The conditional sequences are modulated to decide what types of information or what perspective to focus on when forming summaries to tackle the under-constrained problem in summarization tasks. This framework supports two types of use cases: (1) Comprehensive Perspective, which is a general-purpose case with no user-preference specified, considering summary points from all conversational interlocutors and all mentioned persons; (2) Focus Perspective, positioning the summary based on a user-specified personal named entity, which could be one of the interlocutors or one of the persons mentioned in the conversation. During training, we exploit occurrence planning of personal named entities and coreference information to improve temporal coherence and to minimize hallucination in neural generation. Experimental results show that our proposed framework generates fluent and factually consistent summaries under various planning controls using both objective metrics and human evaluations.
License type:
Attribution 4.0 International (CC BY 4.0)
Funding Info:
This research is supported by core funding from: I2R
Grant Reference no. : SC20/20-130610-CORE
Description:
ISBN:
2021.emnlp-main.8